Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

TL;DR

This paper demonstrates how deep learning combined with electron beam manipulation enables precise tracking and analysis of individual Si atom motion in graphene, revealing atomic structural changes and defect configurations.

Contribution

It introduces a novel deep learning approach to analyze atomic structure evolution during electron beam induced Si atom motion in graphene, enabling defect library creation and symmetry breaking exploration.

Findings

Deep learning enables rapid analysis of atomic distortions.

Deterministic Si atom motion can be controlled and tracked.

Structural evolution and defect configurations are statistically characterized.

Abstract

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx